Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces

TL;DR

This paper explores how agentic AI shifts user interaction from routine responses to proactive communication for oversight and explanation, emphasizing trust and human agency.

Contribution

It introduces a communication perspective on agentic AI, proposing explanation types and customization features to manage trust and perceived agency.

Findings

Agentic AI reduces routine interaction but increases communication for oversight.

Three explanation types are proposed: action-process, uncertainty, and coordination.

Customization of explanations can help preserve human agency.

Abstract

AI systems have long been expected to interact with users, answering questions, generating content, and continuing (social) conversations. Agentic AI, however, breaks from this expectation, as its primary objective is workflow execution on behalf of the users. If a system becomes more agentic, do users need less interaction with the system? Our answer is: less routine back-and-forth, but more communication for oversight and explanation, as agentic AI proactively acts, not just responds. Grounded in a communication perspective, we discuss how users perceive the communicative roles of AI systems (whether as the source of actions or merely a channel), and how this can shape trust. Because agentic AI can play multiple communicative roles, it can complicate this source perception and introduce potential risks. To address this, we propose three types of explanations that agentic AI needs to incorporate (action-process, uncertainty, and coordination), and suggest that customization affordances that allow users to decide when and which explanations they see may be key to preserving human agency as AI autonomy increases.